Patients suffering from Chronic Kidney Disease (CKD) may be at a higher risk of developing atherosclerotic cardiovascular disease (ASCVD). However, no ASCVD risk prediction models had been created in patients with CKD to guide clinical management and prevention. For a study, researchers created and validated 10-year ASCVD risk prediction models in patients with CKD who did not have a self-reported cardiovascular illness from the Chronic Renal Insufficiency Cohort (CRIC) research. The first incidence of adjudicated fatal or nonfatal stroke or myocardial infarction was classified as ASCVD. The models made advantage of clinically accessible factors as well as new biomarkers. Discrimination, calibration, and net reclassification improvement were used to evaluate model performance.

About 252 of the 2,604 patients included in the analysis (mean age 55.8; 52.0% male) developed incident ASCVD within 10 years after baseline. In comparison to the American College of Cardiology/American Heart Association pooled cohort equations (AUC=0.730), a model with coefficients computed inside the CRIC sample demonstrated greater discrimination (P=0.03), obtaining an AUC of 0.736. (95%confidence interval [CI], 0.649 to 0.826). The AUC of the CRIC model constructed utilizing clinically accessible variables was 0.760. (95% CI, 0.678 to 0.851). The AUC for the CRIC biomarker-enriched model was 0.771 (95% CI, 0.674 to 0.853), which was considerably higher than the clinical model (P=0.001). When compared to the pooled cohort equations, both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). The 10-year ASCVD risk prediction models established in patients with CKD, which included new renal and cardiac biomarkers, outperformed equations developed for the general population using just conventional risk variables.